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Joining the dots.
Connecting what’s happening in AI to what it actually means for your business: news, insights, and the occasional event.
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Giving an AI Agent Access? Treat It Like a Hire
Australian firms are moving fast on AI structure and slow on control. Here is how boards close the gap before agents get standing access.
The CSIRO Data That Undercuts the AI Redundancy Story
New Australian research shows firms adopting AI are advertising more roles, not fewer, and asking for broader skills.
When AI Layoffs Boomerang: Lessons From CBA's Voice Bot Reversal
Premature headcount cuts based on AI performance claims are proving expensive to unwind, and the discipline to avoid that is learnable.
The great AI cost blow-out of 2026
Halfway through 2026, businesses are tearing through their AI budgets at a pace nobody planned for. The cause is a lesson in adopting AI without measuring it.
Shadow AI: what your team is already using
About half your team is using AI you never approved, and the heaviest users are often your executives. Banning it backfires. Here's the grown-up response, and the 2025 numbers behind it.
7 reasons to adopt AI with a partner, not alone
Going it alone with AI is really just winging it, and past a certain size that gets expensive. Our founder, Colin Cardwell, on when to do it yourself, when to get help, and why expertise-led adoption succeeds three times as often.
Who's liable when your AI gets it wrong?
2026 has brought a wave of lawsuits and fines over AI that misled people. The pattern is clear: when your AI gets it wrong, the business is the one that pays.
The AI risks most businesses aren't pricing in
The expensive AI risks are the quiet ones, the kind that stay invisible until they aren't. In 2025 alone, courts logged more than 700 cases of AI inventing facts. Here are the risks worth pricing in before they arrive.
Three quarters of AI strategies are 'more for show'
A 2026 survey found three quarters of executives admit their AI strategy is more for show than real direction, even as most pour over a million dollars a year into it. The gap between spending and strategy is the whole story.
The 7 questions every business asks before adopting AI
Before any business adopts AI seriously, the same seven questions come up. Here are straight answers to all of them, and where to dig deeper on each.
How to tell if your AI is actually working
Most businesses measure how much their AI gets used. Almost none measure whether it works. MIT found 95% of AI pilots show no bottom-line impact, and the fix is to measure outcomes, not activity.
Finding the AI opportunities worth chasing
Most businesses spend their AI budget where it is most visible, not where it pays. MIT found the biggest returns hiding in the back office. Here is how to find the opportunities actually worth chasing.
Australian businesses are adopting AI fast, but trust is the handbrake
AI use among Australian SMEs has surged past two thirds, and most report real productivity gains. The one thing still holding adoption back is trust, which happens to be the fixable part.
An AI business case that survives the budget meeting
Most AI spending can't survive a hard question about its return, which is why so much of it gets cut. Here is how to build an AI business case that holds up when the budget is tight.
Why your data strategy makes or breaks AI
AI doesn't clean up a data mess, it scales it. Gartner expects 60% of AI projects without AI-ready data to be abandoned. Here's what AI-ready actually means, and why you don't need perfect data to start.
Why we say AI learning, not training
A training day teaches a tool. It doesn't change how a business works by Friday. MIT found the real barrier to AI isn't the technology, it's the learning gap, and that needs something more ongoing than a course.
The AI Champion model: adoption that spreads from the inside
AI adoption mandated from the top tends to stall. The most reliable way to spread it is sideways, through enthusiasts inside your own team. Here's how the AI Champion model works.
Leading a team through AI change
AI adoption fails more often as a people problem than a technology one. The research calls it a trust problem. Here's how to lead a team through the change without losing them along the way.
AI-mature firms are pulling ahead, and the gap is widening
The businesses getting AI right aren't just saving time. They're growing faster, and the lead compounds every quarter.
How we built The GAiGE
We kept hitting the same wall with clients: plenty of AI in use, no way to tell if it was working. So we built the measurement layer we wished existed. Here's how, and why it works the way it does.
Why AI adoption needs your leaders
In 2025, executives turned out to be the heaviest users of unsanctioned AI. Using it themselves, though, is not the same as leading it. Why adoption stalls without leaders, and what driving it actually takes.
Wathaga: building AI around data sovereignty
First Nations communities needed to measure their work without handing their stories to a machine. With Kowa Collaboration, we built a platform that puts AI to work while leaving ownership exactly where it belongs.
Choosing AI tools you won't regret in six months
The AI tool that looks essential today can be the wrong call by next quarter. Here's how to choose for fit and flexibility, so you don't end up locked into yesterday's best option.
Why we built The GAiGE
Plenty of AI tools in use, no clear way to tell whether any of them were working. So we built the measurement layer we kept wishing for.
Automation vs agents: which, and when
Agent is the word of the year in AI, and most businesses asking for one really need a simple automation. Here's the real difference, and how to tell which your problem calls for.
When to build custom AI (and the vibe-coding trap)
AI makes building software feel effortless, and that's the danger. Veracode found 45% of AI-generated code carries security flaws. Here's when building custom AI is worth it, and how to avoid the vibe-coding trap.
The gap nobody's measuring
22% vs 67%. The difference between AI that works and AI that doesn't comes down to expertise, not ambition.
Three quiet ways pilots stall
Promising experiments rarely fail on the technology. They stall in the gap between the demo and the workflow.